Machine Operator Feedback Correlation Using Multi-Source Machine Learning
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Solution Overview
Problem
Existing machine operator feedback systems fail to account for various factors influencing optimal performance, leading to suboptimal operation of vehicles like planes.
Innovation Solution
An apparatus and method utilizing a processor, memory, and sensing devices to receive, classify, and generate feedback correlations through a machine learning model, providing insights to improve operator performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional feedback systems are used for machine operator monitoring, then the system structure remains simple, but the system fails to account for multiple factors influencing operator performance and cannot provide comprehensive feedback
Solution Approach 1:
The patent segments performance data into multiple distinct categories (physiological data, environmental data, task performance data, equipment data) that can be independently collected, processed, and analyzed. This segmentation allows the system to comprehensively capture various factors influencing operator performance while maintaining manageable data processing through dedicated sensors and processing modules for each category.
Solution Approach 2:
The patent implements a multi-functional integrated platform that simultaneously performs data collection from diverse sources, real-time processing, classification into multiple performance categories, correlation analysis between different data types, and generation of comprehensive feedback reports. This universal system replaces multiple separate monitoring systems with one unified platform that handles all aspects of operator performance assessment.
2Measurement precision
If comprehensive performance data collection is implemented to account for multiple influencing factors, then operator performance understanding is improved, but data processing complexity and computational requirements increase
Solution Approach 1:
The patent applies preliminary classification of performance data into predetermined categories (physiological, environmental, task performance, equipment) before detailed analysis. This preliminary organization structures the comprehensive data set in advance, making subsequent correlation analysis and pattern recognition more efficient and reducing the computational burden of processing raw multi-source data.
Solution Approach 2:
The patent implements iterative feedback loops where initial performance assessments are continuously refined based on correlated data from multiple sources. The system generates preliminary feedback, compares it with additional data categories, and adjusts performance evaluations accordingly, improving measurement precision through repeated cycles of data collection, analysis, and refinement.
Data Source
AI summary
In an aspect, an apparatus for machine operator feedback correlation is presented. An apparatus includes at least a processor and a memory communicatively connected to the at least a processor. A memory contains instructions configuring at least a processor to receive, through a sensing device, performance data of at least a machine operator. At least a processor is configured to classify performance data to a performance category through a performance classifier. At least a processor is configured to calculate a performance determination. At least a processor is configured to generate a feedback correlation through a machine operator feedback correlation machine learning model. At least a processor is configured to provide a feedback correlation to a user through a display device.